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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

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Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention

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Where new brain files go. Decision protocol for filing brain pages by primary subject, not by format or source. Reference for all brain-writing skills.

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Search and write the company knowledge brain. Use for any question about the org, people, projects, decisions, or history, and to persist durable knowledge beyond this scope's notebook.

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Ingest a voice note with exact-phrasing preservation (never paraphrased). Routes content to originals/, concepts/, people/, companies/, ideas/, personal/, or voice-notes/ based on a decision tree. The user's exact words are the signal.

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Reusable pattern for presenting the user with explicit choices and gating execution until they respond. Used by other skills when a decision point requires human input before proceeding. Platform-agnostic — works on Telegram (inline buttons), Discord, CLI, or any agent with a message tool.

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Pre-write quality gate for content entering the brain. No raw copies: a bare cp/mv into the brain repo is a bug. Before any new page lands, resolve named entities registry-first (a vector score is a floor for prose, never a gate for named things), then run the read-the-top-hit dedup decision tree (clear-dup / plausible-dup / clear). Owns dedup; delegates enrichment to the shipped ingestion skills. Routing convention, not an operation-boundary enforcement.

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Analyze CSV files in /mnt/data and return concise numeric summaries.

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Build a concise prior authorization packet from local case files and payer policy docs.

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Works the bill pile from the AP inbox all the way to a staged payment run — reads and codes every bill, then checks the cash position before a dollar is committed, then stages the run for one-click approval and keeps the books tidy for close. Coding and paying are two separate decisions with two separate approvals, and the cash check sits between them on purpose. Runs on the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books) and a mail connector (Gmail or M365), deepens with Expensify and Ramp, and works from uploaded PDFs or phone photos with the run sheet exported for manual entry. Use it when the owner says "pay the bills," "the bills are piling up," "what do I owe," "who needs to get paid this week," "can I afford to pay these," or forwards a stack of vendor invoices.

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Full ICP-to-leads pipeline. Describe your ideal customer in plain English and get a ranked table of enriched decision-maker leads with emails and phone numbers.

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Turns what is actually selling into a reorder decision and gets it all the way into the books — computes sales velocity and conservative stockout dates per item, sizes the reorder, drafts the purchase order and the vendor email, and then stages the matching entry so the bill matches against the PO when it arrives. Flags the money sitting still too, so dead stock gets cleared instead of reordered. Runs on Shopify, Square, or an uploaded sales and stock CSV, and deepens with QuickBooks, Gmail or M365, Calendar, and NetSuite. No PO is sent and no vendor is emailed without approval. Use it when the owner says "what do I need to reorder," "am I going to run out," "restock," "we keep running out of filters," "draft a PO for my supplier," or "how much cash is sitting in the warehouse."

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Closes the books and turns them into a decision as a three-link chain — month-end-prep reconciles the ledger against every connected payment processor and writes the P&L narrative, cash-flow-snapshot then refreshes the 30/60/90-day forecast off the newly closed numbers rather than raw ones, and report-builder publishes and distributes the close packet. Requires a ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books) and uses Gusto, PayPal, Ramp, Shopify, Square, and Stripe when connected, falling back to statement or CSV uploads. Trigger on "close the month," "month-end," "close the books," "reconcile," "send the close packet to my accountant," or when the owner asks why revenue or margin moved last month. An owner who wants only the reconciliation and its flags, with no forecast refresh and no packet distribution, routes to month-end-prep directly.

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Identify, assess, and mitigate operational risks. Trigger with "what are the risks", "risk assessment", "risk register", "what could go wrong", or when the user is evaluating risks associated with a project, vendor, process, or decision.

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Generate a status report with KPIs, risks, and action items. Use when writing a weekly or monthly update for leadership, summarizing project health with green/yellow/red status, surfacing risks and decisions that need stakeholder attention, or turning a pile of project tracker activity into a readable narrative.

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Search across all connected sources in one query. Trigger with "find that doc about...", "what did we decide on...", "where was the conversation about...", or when looking for a decision, document, or discussion that could live in chat, email, cloud storage, or a project tracker.

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Generate a daily or weekly digest of activity across all connected sources. Use when catching up after time away, starting the day and wanting a summary of mentions and action items, or reviewing a week's decisions and document updates grouped by project.

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Save an insight, decision, or learning to agentmemory's long-term storage with searchable concept tags. Use when the user says "remember this", "save this", "note that", "don't forget", or wants to preserve knowledge for future sessions.

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Update docs/CHANGELOG.md from git history, GitHub releases, and code diffs. Use when: writing release notes, syncing the latest changelog entry, summarizing a new tag, or keeping changelog wording concise and consistent.

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Write contextual commits that capture intent, decisions, and constraints alongside code changes. Use when committing code, finishing a task, or when the user asks to commit. Extends Conventional Commits with structured action lines in the commit body that preserve WHY code was written, not just WHAT changed.

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Review a PR the way a seasoned maintainer would — build an independent model before seeing the diff, investigate the implementation and history, verify material claims, and return a concise briefing. Use for any PR review, self-review, or re-review.

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Investigate a GitHub issue or reported bug the way a seasoned maintainer would — form an independent model before reading the thread's theories, trace the mechanism and history, verify the diagnosis, and return a concise briefing with a verdict and fix direction. Use for triage, root-cause analysis, or re-investigation.

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Use early when debugging a medium or hard bug, especially when tests alone may not reveal the real runtime failure. Trigger this before extended TDD iteration when a bug involves runtime state, ordering, persistence, streaming, concurrency, UI/manual reproduction, external services, or when a red or newly passing test may not model the real issue. Skip only when the root cause is already directly proven by a stack trace or deterministic test that exercises the real runtime path.

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EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider / deployer / importer / distributor / authorized representative), what are the active obligations and deadlines? Use during AI system intake review, when planning conformity assessment, or when scoping deployer obligations. Cites Articles + Annexes for every output. NOT executive AI strategy (see chief-ai-officer-advisor). NOT a legal substitute.

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